metadata
license: mit
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-banking77-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9321428571428572
widget:
- text: 'Can I track the card you sent to me? '
example_title: Card Arrival Example - English
- text: 'Posso tracciare la carta che mi avete spedito? '
example_title: Card Arrival Example - Italian
- text: Can you explain your exchange rate policy to me?
example_title: Exchange Rate Example - English
- text: Potete spiegarmi la vostra politica dei tassi di cambio?
example_title: Exchange Rate Example - Italian
- text: I can't pay by my credit card
example_title: Card Not Working Example - English
- text: Non riesco a pagare con la mia carta di credito
example_title: Card Not Working Example - Italian
xlm-roberta-base-banking77-classification
This model is a fine-tuned version of xlm-roberta-base on the banking77 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3034
- Accuracy: 0.9321
- F1 Score: 0.9321
Model description
More information needed
Intended uses & limitations
The model can be used on text classification. In particular is fine tuned on banking domain.
Training and evaluation data
The dataset used is banking77
The 77 labels are:
| label | intent |
|---|---|
| 0 | activate_my_card |
| 1 | age_limit |
| 2 | apple_pay_or_google_pay |
| 3 | atm_support |
| 4 | automatic_top_up |
| 5 | balance_not_updated_after_bank_transfer |
| 6 | balance_not_updated_after_cheque_or_cash_deposit |
| 7 | beneficiary_not_allowed |
| 8 | cancel_transfer |
| 9 | card_about_to_expire |
| 10 | card_acceptance |
| 11 | card_arrival |
| 12 | card_delivery_estimate |
| 13 | card_linking |
| 14 | card_not_working |
| 15 | card_payment_fee_charged |
| 16 | card_payment_not_recognised |
| 17 | card_payment_wrong_exchange_rate |
| 18 | card_swallowed |
| 19 | cash_withdrawal_charge |
| 20 | cash_withdrawal_not_recognised |
| 21 | change_pin |
| 22 | compromised_card |
| 23 | contactless_not_working |
| 24 | country_support |
| 25 | declined_card_payment |
| 26 | declined_cash_withdrawal |
| 27 | declined_transfer |
| 28 | direct_debit_payment_not_recognised |
| 29 | disposable_card_limits |
| 30 | edit_personal_details |
| 31 | exchange_charge |
| 32 | exchange_rate |
| 33 | exchange_via_app |
| 34 | extra_charge_on_statement |
| 35 | failed_transfer |
| 36 | fiat_currency_support |
| 37 | get_disposable_virtual_card |
| 38 | get_physical_card |
| 39 | getting_spare_card |
| 40 | getting_virtual_card |
| 41 | lost_or_stolen_card |
| 42 | lost_or_stolen_phone |
| 43 | order_physical_card |
| 44 | passcode_forgotten |
| 45 | pending_card_payment |
| 46 | pending_cash_withdrawal |
| 47 | pending_top_up |
| 48 | pending_transfer |
| 49 | pin_blocked |
| 50 | receiving_money |
| 51 | Refund_not_showing_up |
| 52 | request_refund |
| 53 | reverted_card_payment? |
| 54 | supported_cards_and_currencies |
| 55 | terminate_account |
| 56 | top_up_by_bank_transfer_charge |
| 57 | top_up_by_card_charge |
| 58 | top_up_by_cash_or_cheque |
| 59 | top_up_failed |
| 60 | top_up_limits |
| 61 | top_up_reverted |
| 62 | topping_up_by_card |
| 63 | transaction_charged_twice |
| 64 | transfer_fee_charged |
| 65 | transfer_into_account |
| 66 | transfer_not_received_by_recipient |
| 67 | transfer_timing |
| 68 | unable_to_verify_identity |
| 69 | verify_my_identity |
| 70 | verify_source_of_funds |
| 71 | verify_top_up |
| 72 | virtual_card_not_working |
| 73 | visa_or_mastercard |
| 74 | why_verify_identity |
| 75 | wrong_amount_of_cash_received |
| 76 | wrong_exchange_rate_for_cash_withdrawal |
Training procedure
from transformers import pipeline
pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification")
pipe("Non riesco a pagare con la carta di credito")
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 3.8002 | 1.0 | 157 | 2.7771 | 0.5159 | 0.4483 |
| 2.4006 | 2.0 | 314 | 1.6937 | 0.7140 | 0.6720 |
| 1.4633 | 3.0 | 471 | 1.0385 | 0.8308 | 0.8153 |
| 0.9234 | 4.0 | 628 | 0.7008 | 0.8789 | 0.8761 |
| 0.6163 | 5.0 | 785 | 0.5029 | 0.9068 | 0.9063 |
| 0.4282 | 6.0 | 942 | 0.4084 | 0.9123 | 0.9125 |
| 0.3203 | 7.0 | 1099 | 0.3515 | 0.9253 | 0.9253 |
| 0.245 | 8.0 | 1256 | 0.3295 | 0.9227 | 0.9225 |
| 0.1863 | 9.0 | 1413 | 0.3092 | 0.9269 | 0.9269 |
| 0.1518 | 10.0 | 1570 | 0.2901 | 0.9338 | 0.9338 |
| 0.1179 | 11.0 | 1727 | 0.2938 | 0.9318 | 0.9319 |
| 0.0969 | 12.0 | 1884 | 0.2906 | 0.9328 | 0.9328 |
| 0.0805 | 13.0 | 2041 | 0.2963 | 0.9295 | 0.9295 |
| 0.063 | 14.0 | 2198 | 0.2998 | 0.9289 | 0.9288 |
| 0.0554 | 15.0 | 2355 | 0.2933 | 0.9351 | 0.9349 |
| 0.046 | 16.0 | 2512 | 0.2960 | 0.9328 | 0.9326 |
| 0.04 | 17.0 | 2669 | 0.3032 | 0.9318 | 0.9318 |
| 0.035 | 18.0 | 2826 | 0.3061 | 0.9312 | 0.9312 |
| 0.0317 | 19.0 | 2983 | 0.3030 | 0.9331 | 0.9330 |
| 0.0315 | 20.0 | 3140 | 0.3034 | 0.9321 | 0.9321 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1